Dataset Information

Low-dose coronary artery calcium scoring compared to the standard protocol.

ABSTRACT:

Background

We aimed to compare coronary artery calcium scoring (CACS) with computed tomography (CT) with 80 and 120 kVp in a large patient population and to establish whether there is a difference in risk classification between the two scores.

Methods

Patients with suspected CAD undergoing MPS were included. All underwent standard CACS assessment with 120-kVp tube voltage and with 80 kVp. Two datasets (low-dose and standard) were generated and compared. Risk classes (0 to 25, 25 to 50, 50 to 75, 75 to 90, and > 90%) were recorded.

Results

1511 patients were included (793 males, age 69 ± 9.1 years). There was a very good correlation between scores calculated with 120 and 80 kVp (R = 0.94, R² = 0.88, P < .001), with Bland-Altman limits of agreement of - 563.5 to 871.9 and a bias of - 154.2. The proportion of patients assigned to the < 25% percentile class (P = .03) and with CACS = 0 differed between the two protocols (n = 264 vs 437, P < .001).

Conclusion

In a large patient population, despite a good correlation between CACS calculated with standard and low-dose CT, there is a systematic underestimation of CACS with the low-dose protocol. This may have an impact especially on the prognostic value of the calcium score, and the established "power of zero" may no longer be warranted if CACS is assessed with low-dose CT.

SUBMITTER: Allio IR

PROVIDER: S-EPMC10261226 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Low-dose coronary artery calcium scoring compared to the standard protocol.

Allio Ileana Rosely IR Caobelli Federico F Popescu Cristina Elena CE Haaf Philip P Alberts Ian I Frey Simon M SM Zellweger Michael J MJ

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology 20221026 3

<h4>Background</h4>We aimed to compare coronary artery calcium scoring (CACS) with computed tomography (CT) with 80 and 120 kVp in a large patient population and to establish whether there is a difference in risk classification between the two scores.<h4>Methods</h4>Patients with suspected CAD undergoing MPS were included. All underwent standard CACS assessment with 120-kVp tube voltage and with 80 kVp. Two datasets (low-dose and standard) were generated and compared. Risk classes (0 to 25, 25 t ...[more]

PMID: 36289163

Similar Datasets

Project description:In this prospective, pilot study, we tested a kV-independent coronary artery calcium scoring CT protocol, using a novel reconstruction kernel (Sa36f). From December 2018 to November 2019, we performed an additional research scan in 61 patients undergoing clinical calcium scanning. For the standard protocol (120 kVp), images were reconstructed with a standard, medium-sharp kernel (Qr36d). For the research protocol (automated kVp selection), images were reconstructed with a novel kernel (Sa36f). Research scans were sequentially performed using a higher (cohort A, n = 31) and a lower (cohort B, n = 30) dose optimizer setting within the automatic system with customizable kV selection. Agatston scores, coronary calcium volumes, and radiation exposure of the standard and research protocol were compared. A phantom study was conducted to determine inter-scan variability. There was excellent correlation for the Agatston score between the two protocols (r = 0.99); however, the standard protocol resulted in slightly higher Agatston scores (29.4 [0-139.0] vs 17.4 [0-158.2], p = 0.028). The median calcium volumes were similar (11.5 [0-109.2] vs 11.2 [0-118.0] mm3; p = 0.176), and the number of calcified lesions was not significantly different (p = 0.092). One patient was reclassified to another risk category. The research protocol could be performed at a lower kV and resulted in a substantially lower radiation exposure, with a median volumetric CT dose index of 4.1 vs 5.2 mGy, respectively (p < 0.001). Our results showed that a consistent coronary calcium scoring can be achieved using a kV-independent protocol that lowers radiation doses compared to the standard protocol. KEY POINTS: • The Sa36f kernel enables kV-independent Agatston scoring without changing the original Agatston weighting threshold. • Agatston scores and calcium volumes of the standard and research protocols showed an excellent correlation. • The research protocol resulted in a significant reduction in radiation exposure with a mean reduction of 22% in DLP and 25% in CTDIvol.

Project description:BackgroundHypertensive disorders of pregnancy are important causes of maternal morbidity and mortality, as well as preterm birth, the leading cause of death for children under 5 years globally. The World Health Organization currently recommends that pregnant women receive high-dose calcium supplementation (1500-2000 mg elemental calcium) for prevention of preeclampsia in populations with low dietary calcium intake. Trials of low-dose calcium supplementation (< 1000 mg elemental calcium/day) during pregnancy have also shown similar reductions in the risk of preeclampsia; however, no trials to date have directly compared low-dose to the standard high-dose calcium supplementation. Our objective is to assess the non-inferiority of low-dose as compared to standard high-dose calcium supplementation in pregnancy.Methods/designWe will conduct two independent trials in Bangalore, India (n = 11,000 pregnancies), and Dar es Salaam, Tanzania (n = 11,000 pregnancies). The trial designs are individually randomized, parallel group, quadruple-blind, non-inferiority trials of low-dose calcium supplementation (500 mg elemental calcium/day) as compared to standard high-dose calcium supplementation (1500 mg elemental calcium/day) among nulliparous pregnant women. Pregnant women will be enrolled in the trial before 20 weeks of gestation and will receive the randomized calcium regimen from randomization until the time of delivery. The co-primary outcomes are (i) preeclampsia and (ii) preterm birth; we will test non-inferiority of the primary outcomes for low-dose as compared to the standard high-dose supplementation regimen in each trial. The trials' secondary outcomes include gestational hypertension, severe features of preeclampsia, pregnancy-related death, third trimester severe anemia, fetal death, stillbirth, low birthweight, small-for-gestational age birth, and infant death.DiscussionThe trials will provide causal evidence on the non-inferiority of low-dose as compared to the standard high-dose supplementation in India and Tanzania. A single tablet, low-dose calcium supplementation regimen may improve individual-level adherence, reduce programmatic costs, and ultimately expand implementation of routine calcium supplementation in pregnancy in populations with low dietary calcium intake.Trial registrationClinicalTrials.gov identifier: NCT03350516 ; registered on 22 November 2018. Clinical Trials Registry-India identifier: CTRI/2018/02/012119 ; registered on 23 February 2018. Tanzania Medicines and Medical Devices Authority Trials Registry identifier: TFDA0018/CTR/0010/5 ; registered on 20 December 2018.

Project description:Deep learning methods have demonstrated the ability to perform accurate coronary artery calcium (CAC) scoring. However, these methods require large and representative training data hampering applicability to diverse CT scans showing the heart and the coronary arteries. Training methods that accurately score CAC in cross-domain settings remains challenging. To address this, we present an unsupervised domain adaptation method that learns to perform CAC scoring in coronary CT angiography (CCTA) from non-contrast CT (NCCT). To address the domain shift between NCCT (source) domain and CCTA (target) domain, feature distributions are aligned between two domains using adversarial learning. A CAC scoring convolutional neural network is divided into a feature generator that maps input images to features in the latent space and a classifier that estimates predictions from the extracted features. For adversarial learning, a discriminator is used to distinguish the features between source and target domains. Hence, the feature generator aims to extract features with aligned distributions to fool the discriminator. The network is trained with adversarial loss as the objective function and a classification loss on the source domain as a constraint for adversarial learning. In the experiments, three data sets were used. The network is trained with 1,687 labeled chest NCCT scans from the National Lung Screening Trial. Furthermore, 200 labeled cardiac NCCT scans and 200 unlabeled CCTA scans were used to train the generator and the discriminator for unsupervised domain adaptation. Finally, a data set containing 313 manually labeled CCTA scans was used for testing. Directly applying the CAC scoring network trained on NCCT to CCTA led to a sensitivity of 0.41 and an average false positive volume 140 mm3/scan. The proposed method improved the sensitivity to 0.80 and reduced average false positive volume of 20 mm3/scan. The results indicate that the unsupervised domain adaptation approach enables automatic CAC scoring in contrast enhanced CT while learning from a large and diverse set of CT scans without contrast. This may allow for better utilization of existing annotated data sets and extend the applicability of automatic CAC scoring to contrast-enhanced CT scans without the need for additional manual annotations. The code is publicly available at https://github.com/qurAI-amsterdam/CACscoringUsingDomainAdaptation.

Project description:ObjectivesCoronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.MethodsFully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).ResultsOf 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.ConclusionsThis artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation.Key points• Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.

Dataset Information

Low-dose coronary artery calcium scoring compared to the standard protocol.

Background

Methods

Results

Conclusion

Publications

Low-dose coronary artery calcium scoring compared to the standard protocol.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets